Importing all required libraries

import sys
import numpy as np 
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import folium

loading in dataset

df = pd.read_csv('COVID19 cases.csv',index_col = 0)
df_pop = pd.read_csv('NeighbourhoodPopulation.csv',index_col = 0)
df_pop

	population	population density
Neighbourhood
Agincourt North	29113	3929
Agincourt South-Malvern West	23757	3034
Alderwood	12054	2435
Annex	30526	10863
Banbury-Don Mills	27695	2775
...	...	...
Wychwood	14349	8541
Yonge-Eglinton	11817	7162
Yonge-St.Clair	12528	10708
York University Heights	27593	2086
Yorkdale-Glen Park	14804	2451

140 rows × 2 columns

 local = df["Neighbourhood Name"] == "Humewood-Cedarvale"
 active = df["Outcome"] == "ACTIVE"
 travel = df["Source of Infection"] == "Travel"
 outbreak = df["Outbreak Associated"] == "Outbreak Associated"

df['count'] = 1
# creating new dataframe with all covid cases by neighbourhood
df_Neighbourhood = df.groupby('Neighbourhood Name',axis = 0).count() 
df_Neighbourhood.drop(['Assigned_ID', 'Outbreak Associated','Age Group', 'FSA', 'Source of Infection', 'Classification', 'Episode Date', 'Reported Date','Client Gender', 'Outcome','Currently Hospitalized', 'Currently in ICU', 'Currently Intubated','Ever Hospitalized','Ever in ICU','Ever Intubated'],axis = 1, inplace = True)
df_Neighbourhood = df_Neighbourhood.sort_values(by = 'count', ascending = False)
df_Neighbourhood.reset_index(level=0, inplace=True)

# creating new dataframe with all ACTIVE covid cases by neighbourhood
df_NeighbourhoodActive = df[active].groupby('Neighbourhood Name',axis = 0).count() 
df_NeighbourhoodActive.drop(['Assigned_ID', 'Outbreak Associated','Age Group', 'FSA', 'Source of Infection', 'Classification', 'Episode Date', 'Reported Date','Client Gender', 'Outcome','Currently Hospitalized', 'Currently in ICU', 'Currently Intubated','Ever Hospitalized','Ever in ICU','Ever Intubated'],axis = 1, inplace = True)
df_NeighbourhoodActive = df_NeighbourhoodActive.sort_values(by = 'count', ascending = False)
df_NeighbourhoodActive.reset_index(level=0, inplace=True)

# creating new dataframe with all TRAVEL RELATED covid cases by neighbourhood
df_Travel = df[travel].groupby('Neighbourhood Name',axis = 0).count() 
df_Travel.drop(['Assigned_ID', 'Outbreak Associated','Age Group', 'FSA', 'Source of Infection', 'Classification', 'Episode Date', 'Reported Date','Client Gender', 'Outcome','Currently Hospitalized', 'Currently in ICU', 'Currently Intubated','Ever Hospitalized','Ever in ICU','Ever Intubated'],axis = 1, inplace = True)
df_Travel = df_Travel.sort_values(by = 'count', ascending = False)
df_Travel.reset_index(level=0, inplace=True)

# creating new dataframe with all OUTBREAK RELATED covid cases by neighbourhood
df_Outbreak = df[outbreak].groupby('Neighbourhood Name',axis = 0).count() 
df_Outbreak.drop(['Assigned_ID', 'Outbreak Associated','Age Group', 'FSA', 'Source of Infection', 'Classification', 'Episode Date', 'Reported Date','Client Gender', 'Outcome','Currently Hospitalized', 'Currently in ICU', 'Currently Intubated','Ever Hospitalized','Ever in ICU','Ever Intubated'],axis = 1, inplace = True)
df_Outbreak = df_Outbreak.sort_values(by = 'count', ascending = False)
df_Outbreak.reset_index(level=0, inplace=True)

# creating new dataframe with all covid cases by neighbourhood
df_NeighbourhoodNew = df.groupby('Neighbourhood Name',axis = 0).count() 
df_NeighbourhoodNew.drop(['Assigned_ID', 'Outbreak Associated','Age Group', 'FSA', 'Source of Infection', 'Classification', 'Episode Date', 'Reported Date','Client Gender', 'Outcome','Currently Hospitalized', 'Currently in ICU', 'Currently Intubated','Ever Hospitalized','Ever in ICU','Ever Intubated'],axis = 1, inplace = True)
df_NeighbourhoodNew = df_NeighbourhoodNew.sort_values(by = 'Neighbourhood Name', ascending = True)
df_NeighbourhoodNew = pd.concat([df_NeighbourhoodNew, df_pop], axis=1)
df_NeighbourhoodNew.reset_index(level=0, inplace=True)
df_NeighbourhoodNew['Cases per 1000 People'] = df_NeighbourhoodNew['count'].div(df_NeighbourhoodNew['population']).mul(1000)
df_NeighbourhoodNew['Cases per Sqaure KM'] = df_NeighbourhoodNew['count'].div(df_NeighbourhoodNew['population'].div(df_NeighbourhoodNew['population density']))
df_NeighbourhoodNew.rename(columns = {'index': 'Neighbourhood Name'}, inplace = True)
df_NeighbourhoodNew

	Neighbourhood Name	count	population	population density	Cases per 1000 People	Cases per Sqaure KM
0	Agincourt North	714	29113	3929	24.525126	96.359221
1	Agincourt South-Malvern West	579	23757	3034	24.371764	73.943932
2	Alderwood	249	12054	2435	20.657043	50.299900
3	Annex	592	30526	10863	19.393304	210.669462
4	Banbury-Don Mills	420	27695	2775	15.165192	42.083409
...	...	...	...	...	...	...
135	Wychwood	273	14349	8541	19.025716	162.498641
136	Yonge-Eglinton	124	11817	7162	10.493357	75.153423
137	Yonge-St.Clair	135	12528	10708	10.775862	115.387931
138	York University Heights	1606	27593	2086	58.203167	121.411807
139	Yorkdale-Glen Park	829	14804	2451	55.998379	137.252026

140 rows × 6 columns

neighbourhood_map = folium.Map(location=[43.6532, -79.3832], zoom_start=11)
casesperkm_map = folium.Map(location=[43.6532, -79.3832], zoom_start=11)
casesper1000p_map = folium.Map(location=[43.6532, -79.3832], zoom_start=11)

toronto_geo = os.path.join('toronto_neighbourhood.geojson')

v1 = np.linspace(df_Neighbourhood['count'].min(),
                              df_Neighbourhood['count'].max(),
                              10, dtype=int)
v1 = v1.tolist() # change the numpy array to a list
v1[-1] = v1[-1] + 1 # make sure that the last value of the list is greater than the maximum immigration

v2 = np.linspace(df_NeighbourhoodNew['Cases per Sqaure KM'].min(),
                              df_NeighbourhoodNew['Cases per Sqaure KM'].max(),
                              10, dtype=int)
v2 = v2.tolist() # change the numpy array to a list
v2[-1] = v2[-1] + 1 # make sure that the last value of the list is greater than the maximum immigration

v3 = np.linspace(df_NeighbourhoodNew['Cases per 1000 People'].min(),
                              df_NeighbourhoodNew['Cases per 1000 People'].max(),
                              10, dtype=int)
v3 = v3.tolist() # change the numpy array to a list
v3[-1] = v3[-1] + 1 # make sure that the last value of the list is greater than the maximum immigration

neighbourhood_map.choropleth(
    geo_data = toronto_geo,
    name = 'toronto covid map',
    data = df_Neighbourhood,
    columns=['Neighbourhood Name', 'count'],
    key_on='feature.properties.AREA_NAME',
    threshold_scale = v1,
    fill_color = 'YlOrRd',
    fill_opacity = 0.7,
    line_opacity = 0.2,
    legend_name = 'Toronto Covid Map'
)
casesperkm_map.choropleth(
    geo_data = toronto_geo,
    name = 'toronto covid map',
    data = df_NeighbourhoodNew,
    columns=['Neighbourhood Name', 'Cases per Sqaure KM'],
    key_on='feature.properties.AREA_NAME',
    threshold_scale = v2,
    fill_color = 'YlOrRd',
    fill_opacity = 0.7,
    line_opacity = 0.2,
    legend_name = 'Toronto Active Covid Map'
)
casesper1000p_map.choropleth(
    geo_data = toronto_geo,
    data = df_NeighbourhoodNew,
    columns=['Neighbourhood Name', 'Cases per 1000 People'],
    key_on='feature.properties.AREA_NAME',
    threshold_scale = v3,
    fill_color = 'YlOrRd',
    fill_opacity = 0.7,
    line_opacity = 0.2,
    legend_name = 'Toronto Covid Cases per 1000 people'
)

neighbourhood_map

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casesper1000p_map

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casesperkm_map

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